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dc.contributor.authorThongchai Dumrongpokaphanen_US
dc.contributor.authorAfshin Gholamyen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorHoang Phuong Nguyenen_US
dc.date.accessioned2019-08-05T04:35:15Z-
dc.date.available2019-08-05T04:35:15Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85065607823en_US
dc.identifier.other10.1007/978-3-030-04200-4_9en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065607823&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/65552-
dc.description.abstract© Springer Nature Switzerland AG 2019. In the first approximation, many economic phenomena can be described by linear systems. However, many economic processes are non-linear. So, to get a more accurate description of economic phenomena, it is necessary to take this non-linearity into account. In many economic problems, among many different ways to describe non-linear dynamics, the most efficient turned out to be Hammerstein-type block models, in which the transition from one moment of time to the next consists of several consequent blocks: linear dynamic blocks and blocks describing static non-linear transformations. In this paper, we explain why such models are so efficient in econometrics.en_US
dc.subjectComputer Scienceen_US
dc.titleWhy hammerstein-type block models are so efficient: Case study of financial econometricsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume809en_US
article.stream.affiliationsThang Long Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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